Author: Seymour RosenPublisher:
ISBN:
Category : Science
Languages : en
Pages :
Book Description
Science Workshop Series
Author: Globe FearonPublisher:
ISBN:
Category : Education
Languages : en
Pages : 228
Book Description
This program presents science concepts in areas of biology, earth science, chemistry, and physical science in a logical, easy-to-follow design that challenges without overwhelming. This flexible program consists of 12 student texts that can easily supplement an existing science curriculum or be used as a stand-alone course. Reading Level: 4-5 Interest Level: 6-12
Science Workshop Series: Forms of energy
Author: Seymour RosenPublisher:
ISBN:
Category : Juvenile Nonfiction
Languages : en
Pages : 260
Book Description
This program presents science concepts in areas of biology, earth science, chemistry, and physical science in a logical, easy-to-follow design that challenges without overwhelming. This flexible program consists of 12 student texts that can easily supplement an existing science curriculum or be used as a stand-alone course. Reading Level: 4-5 Interest Level: 6-12
Science Workshop Series
Author: Seymour RosenScience Workshop Series
Author: Globe FearonPublisher:
ISBN:
Category : Biology
Languages : en
Pages : 212
Book Description
This program presents science concepts in areas of biology, earth science, chemistry, and physical science in a logical, easy-to-follow design that challenges without overwhelming. This flexible program consists of 12 student texts that can easily supplement an existing science curriculum or be used as a stand-alone course. Reading Level: 4-5 Interest Level: 6-12
Science Workshop Series
Author: Globe FearonPublisher:
ISBN:
Category : Education
Languages : en
Pages : 228
Book Description
This program presents science concepts in areas of biology, earth science, chemistry, and physical science in a logical, easy-to-follow design that challenges without overwhelming. This flexible program consists of 12 student texts that can easily supplement an existing science curriculum or be used as a stand-alone course. Reading Level: 4-5 Interest Level: 6-12
Mars Sample Handling Protocol Workshop Series
Author: Publisher:
ISBN:
Category : Mars surface samples
Languages : en
Pages : 136
Book Description
Science Workshop Series
Author: Seymour RosenChildren's Mental Health and the Life Course Model: A Virtual Workshop Series
Author: National Academies of Sciences, Engineering, and MedicinePublisher: National Academies Press
ISBN: 0309683378
Category : Medical
Languages : en
Pages : 93
Book Description
With rapidly rising rates of mental health disorders, changing patterns of occurrence, and increasing levels of morbidity, the need for a better understanding of the developmental origins and influence of mental health on children’s behavioral health outcomes has become critical. This need for better understanding extends to both the growing prevalence of mental health disorders as well as the role and impact of neurodevelopmental pathways in their onset and expression. Addressing these changes in disease patterns and effects on children and families will require a multifaceted approach that goes beyond simply making changes to clinical care or adding personnel to the health services system. New policies, financing, and implementation can put established best practices and numerous research findings from around the country into action. The Maternal and Child Health Life Course Intervention Research Network and the Forum for Children's Well-Being at the National Academies of Sciences, Engineering, and Medicine jointly organized a webinar series to explore how mental health disorders develop over the life course, with a special emphasis on prenatal, early, middle, and later childhood development. This series centered on identifying gaps in our knowledge, exploring possible new strategies for using existing data to enhance understanding of the developmental origins of mental disorders, reviewing potential approaches to prevention and optimization, and proposing new ways of framing how to understand, address, and prevent these disorders from a life course development perspective. This publication summarizes the presentations and discussions from the series.
DATA SCIENCE WORKSHOP: Lung Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI
Author: Vivian SiahaanPublisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 294
Book Description
This Data Science Workshop presents a comprehensive journey through lung cancer analysis. Beginning with data exploration, the dataset is thoroughly examined to uncover insights into its structure and contents. The focus then shifts to categorizing features and understanding their distribution patterns, revealing key trends and relationships that could impact the predictive models. To predict lung cancer using machine learning models, an extensive grid search is conducted, fine-tuning model hyperparameters for optimal performance. The iterative process involves training various models, such as K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron, and evaluating their outcomes to select the best-performing approach. Utilizing GridSearchCV aids in systematically optimizing parameters to enhance predictive accuracy. Deep Learning is harnessed through Artificial Neural Networks (ANN), which involve building multi-layered models capable of learning intricate patterns from data. The ANN architecture, comprising input, hidden, and output layers, is designed to capture the complex relationships within the dataset. Metrics like accuracy, precision, recall, and F1-score are employed to comprehensively evaluate model performance. These metrics provide a holistic view of the model's ability to classify lung cancer cases accurately and minimize false positives or negatives. The Graphical User Interface (GUI) aspect of the project is developed using PyQt, enabling user-friendly interactions with the predictive models. The GUI design includes features such as radio buttons for selecting preprocessing options (Raw, Normalization, or Standardization), a combobox for choosing the ANN model type (e.g., CNN 1D), and buttons to initiate training and prediction. The PyQt interface enhances usability by allowing users to visualize predictions, classification reports, confusion matrices, and loss-accuracy plots. The GUI's functionality expands to encompass the entire workflow. It enables data preprocessing by loading and splitting the dataset into training and testing subsets. Users can then select machine learning or deep learning models for training. The trained models are saved for future use to avoid retraining. The interface also facilitates model evaluation, showcasing accuracy scores, classification reports detailing precision and recall, and visualizations depicting loss and accuracy trends over epochs. The project's educational value lies in its comprehensive approach, taking participants through every step of a data science pipeline. Attendees gain insights into data preprocessing, model selection, hyperparameter tuning, and performance evaluation. The integration of machine learning and deep learning methodologies, along with GUI development, provides a well-rounded understanding of creating predictive tools for real-world applications. Participants leave the workshop empowered with the skills to explore and analyze medical datasets, implement machine learning and deep learning models, and build user-friendly interfaces for effective interaction. The workshop bridges the gap between theoretical knowledge and practical implementation, fostering a deeper understanding of data-driven decision-making in the realm of medical diagnostics and classification.
DATA SCIENCE WORKSHOP: Liver Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI
Author: Vivian SiahaanPublisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 353
Book Description
In this project, Data Science Workshop focused on Liver Disease Classification and Prediction, we embarked on a comprehensive journey through various stages of data analysis, model development, and performance evaluation. The workshop aimed to utilize Python and its associated libraries to create a Graphical User Interface (GUI) that facilitates the classification and prediction of liver disease cases. Our exploration began with a thorough examination of the dataset. This entailed importing necessary libraries such as NumPy, Pandas, and Matplotlib for data manipulation, visualization, and preprocessing. The dataset, representing liver-related attributes, was read and its dimensions were checked to ensure data integrity. To gain a preliminary understanding, the dataset's initial rows and column information were displayed. We identified key features such as 'Age', 'Gender', and various biochemical attributes relevant to liver health. The dataset's structure, including data types and non-null counts, was inspected to identify any potential data quality issues. We detected that the 'Albumin_and_Globulin_Ratio' feature had a few missing values, which were subsequently filled with the median value. Our exploration extended to visualizing categorical distributions. Pie charts provided insights into the proportions of healthy and unhealthy liver cases among different gender categories. Stacked bar plots further delved into the connections between 'Total_Bilirubin' categories and the prevalence of liver disease, fostering a deeper understanding of these relationships. Transitioning to predictive modeling, we embarked on constructing machine learning models. Our arsenal included a range of algorithms such as Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting. The data was split into training and testing sets, and each model underwent rigorous evaluation using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Hyperparameter tuning played a pivotal role in model enhancement. We leveraged grid search and cross-validation techniques to identify the best combination of hyperparameters, optimizing model performance. Our focus shifted towards assessing the significance of each feature, using techniques such as feature importance from tree-based models. The workshop didn't halt at machine learning; it delved into deep learning as well. We implemented an Artificial Neural Network (ANN) using the Keras library. This powerful model demonstrated its ability to capture complex relationships within the data. With distinct layers, activation functions, and dropout layers to prevent overfitting, the ANN achieved impressive results in liver disease prediction. Our journey culminated with a comprehensive analysis of model performance. The metrics chosen for evaluation included accuracy, precision, recall, F1-score, and confusion matrix visualizations. These metrics provided a comprehensive view of the model's capability to correctly classify both healthy and unhealthy liver cases. In summary, the Data Science Workshop on Liver Disease Classification and Prediction was a holistic exploration into data preprocessing, feature categorization, machine learning, and deep learning techniques. The culmination of these efforts resulted in the creation of a Python GUI that empowers users to input patient attributes and receive predictions regarding liver health. Through this workshop, participants gained a well-rounded understanding of data science techniques and their application in the field of healthcare.